Business analytics are a subset of analytical applications used to improve business processes by operationalizing decision-making processes. Using modern business analytics, it is possible to quickly get an overview on aggregated data, such as key performance indicators, with added functions of drill-down and drill-through to more detailed information. To provide the data for analysis, business analytics extract, transform, and integrate data from multiple sources. Business analytical applications may apply complex formulae and theories to data that represents past and current trends to generate predictions that can be used to improve business processes, and to unify data that comes from different sources for faster analytics.
Business analytics may include customer relationship analytics, enterprise analytics, supply chain analytics, and marketplace analytics. Customer relationship analytics measure and optimize customer relationships, and can include campaign management, market exploration, and customer retention analysis. Enterprise analytics typically include planning and simulation tools for enterprise applications. Supply chain analytics may include supplier evaluation, spending optimization, demand aggregation, strategic sourcing, inventory analysis, and manufacturing analysis. Marketplace analytics may yield insights about usage of marketplace offerings through bidding, auctioning, and traffic analysis.
In a business analytics application, data often needs to be translated or transformed from a source system's format into a different format for, among other things, unification of multiple data sources and optimized access in analytical applications. For example, data from an SAP R/3 or a Siebel ERP application would need to be transformed for use in an SAP Business Information Warehouse InfoCube. While most conventional business analytics applications include a user interface for creating record transformations to data, these user interfaces are not optimized for creating and providing representations of structure transformations. In data warehouse applications, structural transformations and record transformations are usually separate transformation steps, enlarging the data flow model and making separate user interfaces necessary. Further, present business analytics applications do not provide separate storage of local user interface views having separate layouts.